Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
July 22nd, 2020
Presented here is a protocol to discover the overexpressed driver genes maintaining the established cancer stem-like cells derived from colorectal HT29 cells. RNAseq with available bioinformatics was performed to investigate and screen gene expression networks for elucidating a potential mechanism involved in the survival of targeted tumor cells.
Our protocol utilized RNA-Seq and bioinformatical tools to quickly uncover the overexpressed driver genes that contribute to the formation of stem-like colorectal tumorspheres. This protocol is used to screen a differentially expressed gene and signal pathways to investigate a molecular mechanism for cancer stemness in a selective colorectal cancers. On day seven of culture, use an inverted microscope with a digital cell imaging system to observe and measure the diameter of the tumorspheres.
When the tumorspheres reach a greater than 100 micrometer diameter, treat the culture with 0.25%trypsin. After five minutes at 37 degrees Celsius, neutralize the trypsin with two times the volume of growth medium and count the cells using a hemocytometer. Collect the cells by centrifugation.
And resuspend the pellet at a five times 10 to the four cells per 100 microliters of medium concentration. Add two microliters of the primary antibody of interest to each tube and incubate the cells for 30 minutes at room temperature and 200 revolutions per minute. At the end of the incubation, wash each tube with 900 microliters of PBS and analyze the cells for expression of the protein of interest by flow cytometry according to standard protocols.
For RNA isolation after trypsin collection as just demonstrated, resuspend the cells at a two times 10 to the fifth cells per 50 microliters of PBS concentration and lyse each sample with 200 microliters of lysis buffer supplemented with two microliters of beta mercaptoethanol per tube. After brief vortexing and a five minute incubation at room temperature, collect the lysates by centrifugation and add 200 microliters of 70%ethanol to each tube. Transfer the samples into rapid columns and remove the solvent by centrifugation.
Wash the samples with 400 microliters of wash solution one and 600 microliters of wash solution two to completely remove any non-RNA by centrifugation, followed by an additional two minutes centrifugation under the same centrifuge conditions to remove any residual ethanol. Then resuspend the pellets in 50 microliters of distilled water per tube and centrifuge the sample for one minute before collecting the RNA containing supernatants. To investigate the differential gene expression between tumorsphere cells compared to parental tumor cells after RNA sequencing analysis.
Select genes with a greater than one to minus one log2 fold change with read counts greater than 100 in the tumorsphere and parental groups. And use the commands as indicated. To obtain a volcano plot of the data to determine the differential gene expression.
For drive gene selection in network analyst, select single gene input and copy and paste the selected overexpressed genes with human specified as the organism and official gene symbol as the ID type. Click Upload and proceed. To insert and analyze the data using protein-protein interaction, following genetic protein-protein interaction, use the STRING interactome database with a confidence score cut off of 900 to show the seed genes.
Cross-linking the uploaded genes. The seed genes associating with more individual genes can be selected as driver genes that may be involved in maintaining formation of the tumorsphere. Select Proceed in the mapping overview white in the Background and Force Atlas in the Layout knob.
Then select PANTHER base pair to analyze the upregulation gene group. Tumorspheres of greater than 100 micrometers in diameter form within seven days of culture. Flow cytometric analysis reveals an increase in LGR5 and CD133 expression in HT29-derived tumorspheres compared to parental HT29 cells.
After RNA sequencing, the genes with a greater than one log2 fold change in their up or downregulation with a P-value less than 0.05 as determined by heat map can be plotted to distinguish the significant genes between HT29-derived tumorspheres and parental HT29 cells. In this representative experiment, analysis of the upregulated genes with a greater than one log2 fold resulted in 10 seed genes, cross-linking the gene networks. PANTHER base pair analysis indicated that two genes were associated with negative regulation of apoptosis.
Moreover, the selected genes were consequently validated by quantitative PCR confirming their increased expression in HT29-derived tumorspheres. The differential gene selection is the most important step of the procedures. Remember to use a log2 fold change greater than one with recounts greater than 100 as the criteria.
Using this protocol, the genes selected from the colorectal-derived tumorspheres can be knocked down or overexpressed to uncover their potential roles in tumorsphere formation.
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Overview
This protocol outlines the use of RNA-Seq and bioinformatics to identify overexpressed driver genes in colorectal cancer stem-like cells derived from HT29 cells. The study aims to elucidate the molecular mechanisms underlying cancer stemness in colorectal tumors.
Key Study Components
Area of Science
- Neuroscience
- Oncology
- Genomics
Background
- Cancer stem-like cells play a crucial role in tumor initiation and progression.
- Understanding the gene expression profiles can help in targeting these cells.
- RNA-Seq provides a comprehensive view of the transcriptome.
- Bioinformatics tools are essential for analyzing complex gene expression data.
Purpose of Study
- To discover overexpressed driver genes in colorectal cancer stem-like cells.
- To investigate the molecular mechanisms involved in the survival of targeted tumor cells.
- To enhance understanding of cancer stemness in colorectal cancer.
Methods Used
- RNA sequencing (RNA-Seq) to analyze gene expression.
- Bioinformatics analysis to identify differentially expressed genes.
- Inverted microscopy for observing tumorsphere growth.
- Centrifugation and cell counting using a hemocytometer.
Main Results
- Identification of key overexpressed genes in colorectal tumorspheres.
- Insights into signaling pathways associated with cancer stemness.
- Demonstration of the protocol's effectiveness in studying cancer biology.
- Potential implications for targeted therapies in colorectal cancer.
Conclusions
- The protocol successfully identifies critical genes involved in colorectal cancer stem-like cells.
- Findings contribute to the understanding of cancer biology and potential treatment strategies.
- Further research is needed to explore therapeutic applications of these findings.
Frequently Asked Questions
Executive Industry Relevance
Identifying driver genes in colorectal cancer stem-like cells enables rapid target validation and mechanistic de-risking in oncology drug discovery. This approach supports predictive confidence in early-stage therapeutic hypothesis testing by linking gene expression to functional stemness phenotypes. The method accelerates lead identification by prioritizing genes with demonstrated overexpression in disease-relevant 3D models.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of therapeutic hypotheses by linking overexpressed genes to cancer stemness maintenance in HT29-derived tumorspheres.
- Operational Value: Provides a reproducible workflow for functional target validation using RNA-seq and bioinformatics to pinpoint driver genes.
- Predictive Value: Supports portfolio triage by identifying genes associated with negative regulation of apoptosis, a key mechanism in tumor survival.
Screening & Assay Development
- Scientific Value: Generates quantitative gene expression data with log2 fold change >1 and read counts >100, enabling standardized hit selection criteria.
- Operational Value: Establishes a scalable screening platform for differential gene expression analysis across disease models using tumorsphere formation as a phenotypic readout.
- Assay Readiness: Produces validated biological systems (LGR5+/CD133+ tumorspheres) suitable for downstream compound screening and target modulation studies.
Translational & Preclinical Research
- Translational Continuity: Uses disease-relevant colorectal cancer stem-like cells to bridge discovery and preclinical validation of driver gene function.
- Mechanistic De-risking: Clarifies molecular mechanisms of cancer stemness through pathway analysis (e.g., STRING, PANTHER) of overexpressed gene networks.
- Risk-Adjusted Advancement: Enables knockdown/overexpression studies to validate gene roles in tumorsphere formation before in vivo modeling.
Pipeline & Workflow Integration
The method integrates into the discovery continuum from target identification through lead optimization, providing molecular insights that inform go/no-go decisions in oncology pipelines.
- Discovery Biology: Supports hypothesis testing and pathway clarification by linking gene overexpression to tumorsphere formation and stemness marker expression.
- Screening: Delivers assay-ready, reproducible tumorsphere models with quantitative RNA-seq outputs for reliable compound evaluation.
- Analytics: Enables statistical comparison of gene expression between tumorsphere and parental cells using defined thresholds (log2FC >1, p<0.05) to drive target selection.
- Translational Research: Connects discovery to preclinical validation by confirming gene expression via qPCR and enabling functional perturbation studies.
- Enterprise Reuse: Establishes a reusable bioinformatics-driven pipeline for driver gene discovery applicable to other cancer models beyond colorectal HT29.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence in target validation by reducing mechanistic ambiguity through network and pathway analysis of driver genes.
- Operational Value: Standardizes gene selection criteria (log2FC >1, read counts >100) and enables cross-functional reproducibility in target discovery workflows.
- Strategic Value: Improves capital efficiency by prioritizing targets with strong functional evidence in stemness phenotypes, reducing late-stage failure risk.
- Portfolio Impact: Facilitates risk-adjusted advancement decisions by providing early evidence of gene involvement in apoptosis regulation and tumorsphere maintenance.
Implementation Considerations
- Requires expertise in cancer stem cell culture, RNA-seq library preparation, and bioinformatics analysis using tools like STRING and PANTHER.
- Needs access to inverted microscopy, flow cytometry, centrifuges, hemocytometers, and sequencing infrastructure for tumorsphere characterization and RNA isolation.
- Demands standardization of tumorsphere size criteria (>100 μm) and stemness marker validation (LGR5/CD133) across laboratories for reproducible results.
- Involves adaptation considerations when applying the protocol to other cancer models, including optimization of culture conditions and stemness markers.
- Practical limitations include dependency on sufficient tumorsphere yield for RNA isolation and the need for orthogonal validation (e.g., qPCR) to confirm sequencing results.
